ItemArticle

Fuzzy Computational Intelligence in Personalized Medicine and Diagnosis

Title
Fuzzy Computational Intelligence in Personalized Medicine and Diagnosis
Creator
Bavirthi, Swathi Sowmya; Manikandan, M.; Rajagopal, Manikandan; Leelavathy, S.; Rao, K. Srinivas
Description
The development of fuzzy computational intelligence (FCI) has emerged as an effective method for personalized medicine and diagnosis. FCI effectively handles uncertainty and imprecision in medical data, facilitating patient-specific treatment recommendations. Conventional diagnostic and treatment methods typically rely on fixed threshold-based approaches, which fail to account for individual variations in patient responses, leading to suboptimal treatment outcomes. This study proposes the personalized treatment recommendation using fuzzy logic (PTR-FC) framework for diabetes (DB) patients to address these challenges. The framework integrates patient-specific data such as blood glucose levels, diet, exercise, and medication history into the fuzzy inference system (FIS), supporting personalized treatment recommendations. The treatment plans are dynamically adapted based on individual patient outcomes using linguistic factors and fuzzy rules (FR). The proposed method dynamically adjusts recommendations in real time, potentially enhancing personalized treatment and improving decision-making in DB management. Additionally, it promotes lifestyle modifications while reducing the risk of medication-induced complications. The effectiveness of the proposed method was compared to conventional methods, demonstrating improved treatment accuracy, increased patient adherence, and reduced adverse health risks. The PTR-FC framework offers a more adaptive and effective approach to DB management, ensuring better patient outcomes. 2009 Tsinghua University Press.
Source
Fuzzy Information and Engineering;Volume;17;Issue;4;pp.472-483
Date
01-01-2025
Publisher
Tsinghua University Press
Subject
computational intelligence; diabetes management; fuzzy logic (FL); medical decision support; personalized medicine; treatment optimization
Coverage
Bavirthi S.S., Chaitanya Bharathi Institute of Technology, IT Dept., Hyderabad, 500075, India; Manikandan M., School of Computing, SRM Institute of Science and Technology, Faculty of Engineering and Technology, Department of Computational Intelligence, Chennai, 603203, India; Rajagopal M., School of Business and Management, Christ university, Bangalore, 560029, India; Leelavathy S., Department of AI&DS, Panimalar engineering college, Chennai, 503200, India; Rao K.S., MLR Institute of Technology, Department of Computer Science and Engineering, Hyderabad, 722003, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 16168658;
Format
online
Language
English
Type
Article

Citation

Bavirthi, Swathi Sowmya; Manikandan, M.; Rajagopal, Manikandan; Leelavathy, S.; Rao, K. Srinivas, “Fuzzy Computational Intelligence in Personalized Medicine and Diagnosis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23444.

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